The Neural Relational Inference (NRI) [1]} model assumes a slightly different setting than GTS, MTGNN, and GDN.
Instead of learning a global, static graph over the whole time series, NRI infers a graph per input window.
While this setting is more flexible, it comes at the drawback of having inherent \(O(B \times N^2)\)  memory complexity, where \(B\)  is the batch size.
Thus, NRI can only realistically scale to small graphs, i.e. at most \(N\approx 50\) . NRI is a VAE-based architecture consisting of an encoder module predicting edge probabilities and a decoder module performing the forecasting.
